Biology subjects

Arenberg, J. G.

Publications and source records attributed to Arenberg, J. G..

3 recordsLinked to original sources

bioRxiv2026

Revisiting Analog Electrical Stimulation with Current Focusing in a Guinea Pig Model of Cochlear Implants.

Modern cochlear implants (CIs) use pulsatile stimulation to restore hearing for individuals with severe hearing loss. CIs provide robust speech recognition in quiet but poorly represent temporal fine structure (TFS), needed for challenging listening situations. Analog stimulation preserves the acoustic waveform and may better encode TFS, yet it has not been evaluated combined with modern current-focusing strategies. We compared neural responses in the inferior colliculus (IC) evoked by CI stimuli consisting of 100 pulses/s biphasic pulse trains and 100 cycles/s sinusoidal analog stimulation with monopolar, bipolar, and tripolar electrode configurations in urethane-anesthetized guinea pigs. Following cochlear implantation, multiunit activity was recorded from the tonotopic axis of the central nucleus of the IC using 16-channel silicon probes. Detection thresholds, spread of excitation, vector strength, sustained response percentage, and temporal response properties were quantified. Analog stimulation consistently evoked significantly lower activation thresholds than pulsatile stimulation while maintaining comparable or sometimes narrower spatial selectivity across stimulation modes. In contrast, analog stimulation generated lower vector strength, larger tonic response components, and a pronounced level-dependent polarity effect. At low stimulus levels, responses were dominated by the cathodic phase of the sinusoidal waveform, whereas increasing stimulus level responses were elicited by both phases, producing synchronization at twice the stimulus frequency. These findings demonstrate that stimulation waveform strongly influences temporal coding while having relatively little effect on the spatial distribution of neural activation. These results provide a physiological basis for reexamining analog stimulation as an alternative strategy for cochlear implant sound coding.

neuroscience↗

bioRxiv2025

A deep learning framework for understanding cochlear implants

Sensory prostheses replace dysfunctional sensory organs with electrical stimulation but currently fail to restore normal perception. Outcomes may be limited by stimulation strategies, neural degeneration, or suboptimal decoding by the brain. We propose a deep learning framework to evaluate these issues by estimating best-case outcomes with task-optimized decoders operating on simulated prosthetic input. We applied the framework to cochlear implants - the standard treatment for deafness - by training artificial neural networks to recognize and localize sounds using simulated auditory nerve input. The resulting models exhibited speech recognition and sound localization that was worse than that of normal hearing listeners, and on par with the best human cochlear implant users, with similar results across the three main stimulation strategies in current use. Speech recognition depended heavily on the extent of decoder optimization for implant input, with lesser influence from other factors. The results identify performance limits of current devices and demonstrate a model-guided approach for understanding the limitations and potential of sensory prostheses.

neuroscience↗

bioRxiv2024

Speech in noise performance in adults with cochlear implants using a combined channel deactivation strategy with a variable number of dynamic focused channels

AbstractO_ST_ABSObjectives and MethodsC_ST_ABSCochlear implant listeners show difficulties in understanding speech in noise. Channel interactions from activating overlapping neural populations reduce the signal accuracy necessary to interpret complex signals. Optimizing programming strategies based on focused detection thresholds to reduce channel interactions has led to improved performance. In the current study, two previously suggested methods, channel deactivation and focused dynamic tripolar stimulation, were combined to create three cochlear implant programs. Utilizing an au-tomatic channel selection algorithm from focused detection threshold profiles, three programs were created with the same deactivated channels but varying proportions of channels employing focused stimulation, monopolar, dynamic focused and a mixed program. Thirteen ears in eleven adult cochlear implant listeners with Advanced Bionics HiRes90k devices were tested. Vowel identification and sentence perception in quiet and noise served as outcome measures, and the influences of listening experience, age, clinical consonant-nucleus-consonant performance, and perceptual thresholds on speech performance were assessed. ResultsAcross subjects, different degrees of focusing showed individual performance improvements for vowels and sentences over the monopolar program. However, only slight trends and no significant group improvements were observed. Focused listening benefits were shown for individuals with less cochlear implant experience, and clinically poor performers seem to benefit more from focusing than good per-formers. ConclusionThe current findings suggest that deactivating and focusing subsets of channels improves speech performance for some individuals, especially poor performers, a possi-ble effect of reduced channel interactions. The findings also show that individual performance is largely variable, possibly due to listening experience, age or the underlying detection threshold.

neuroscience↗